Predicting Ambulatory Capacity in Parkinson's Disease to Analyze Progression, Biomarkers, and Trial Design.

BACKGROUND: In Parkinson's disease (PD), gait and balance is impaired, relatively resistant to available treatment and associated with falls and disability. Predictive models of ambulatory progression could enhance understanding of gait/balance disturbances and aid in trial design. OBJECTIVES: To predict trajectories of ambulatory abilities from baseline clinical data in early PD, relate trajectories to clinical milestones, compare biomarkers, and evaluate trajectories for enrichment of clinical trials. METHODS: Data from two multicenter, longitudinal, observational studies were used for model training (Tracking Parkinson's, n = 1598) and external testing (Parkinson's Progression Markers Initiative, n = 407). Models were trained and validated to predict individuals as having a "Progressive" or "Stable" trajectory based on changes of ambulatory capacity scores from the Movement Disorders Society Unified Parkinson's Disease Rating Scale parts II and III. Survival analyses compared time-to-clinical milestones and trial outcomes between predicted trajectories. RESULTS: On external evaluation, a support vector machine model predicted Progressive trajectories using baseline clinical data with an accuracy, weighted-F1 (proportionally weighted harmonic mean of precision and sensitivity), and sensitivity/specificity of 0.735, 0.799, and 0.688/0.739, respectively. Over 4 years, the predicted Progressive trajectory was more likely to experience impaired balance, loss of independence, impaired function and cognition. Baseline dopamine transporter imaging and select biomarkers of neurodegeneration were significantly different between predicted trajectory groups. For an 18-month, randomized (1:1) clinical trial, sample size savings up to 30% were possible when enrollment was enriched for the Progressive trajectory versus no enrichment. CONCLUSIONS: It is possible to predict ambulatory abilities from clinical data that are associated with meaningful outcomes in people with early PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Implementation and evaluation of a smoking cessation checklist implemented within Australian cancer services.

INTRODUCTION: The detrimental impact of smoking tobacco can be mitigated when cancer patients quit smoking. Smoking cessation clinical pathways are inconsistently implemented within Australian cancer services. The aim of this study was to pilot test and evaluate the reach, adoption, and implementation of a smoking cessation checklist within oncology services. METHODS: The checklist was implemented over a 6-month period in medical and radiation oncology services at two metropolitan and one rural hospital. The RE-AIM framework guided the evaluation process. Implementation strategies included training, process mapping, and identifying champions. Evaluation measures included a clinical data audit, surveys, and semi-structured interviews with healthcare professionals (HCPs). RESULTS: Healthcare professionals (HCPs; N = 63; 41% oncologists, 32% nurses, 27% others) completed 1276 checklists with cancer patients between November 2019 and December 2020. Of the 126 (10%) identified current smokers, 34 (27%) accepted a referral to either Quitline, Nicotine Replacement Therapy, to a general practitioner or dedicated HCP for follow-up telephone support. There was variation in screening adoption by HCPs across the three hospitals, with 16%, 92% and 89.5% of patients screened respectively. Contextual factors, such as perceived commitment, role identity, and communication processes appeared to influence the outcomes. CONCLUSION: A checklist is a simple, effective, and versatile intervention used to standardise smoking cessation practices in medical and radiation oncology services. The checklist supports standardisation of referral practices to smoking cessation services for cancer patients by either oncologist and/or nurses.